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The Sussex-Huawei Locomotion-Transportation Recognition Challenge presented a unique opportunity to the activity-recognition community to test their approaches on a large, real-life benchmark dataset with activities different from those typically recognized. The goal of the challenge was to recognize, as accurately as possible, eight locomotion activities (Still, Walk, Run, Bike, Car, Bus, Train, Subway) using smartphone sensor data. This paper describes the method we developed to win this challenge, and provides an analysis of the effectiveness of its components. We used complex feature extraction and selection methods to train classical machine learning models. In addition, we trained deep learning models using a novel end-to-end architecture for deep multimodal spectro-temporal fusion. All the models were fused into an ensemble with the final predictions smoothed by a hidden Markov model to account for temporal dependencies of the activities. The presented method achieved an F1 score of 94.9% on the challenge test data. We tested different sampling frequencies, window sizes, feature types, classification models and the importance of stand-alone sensors and their fusion for the task. Finally, we present an energy-efficient smartphone implementation of the method.

Gas chromatography-combustion-isotope ratio mass spectrometry (GC-C-IRMS) for the analysis of key volatile compounds sampled using headspace solid phase microextraction (HS-SPME) is an appropriate tool for authenticity assessment of apple aromas. The current research characterises 18 laboratory produced and 15 commercial apple recovery aroma samples, establishes a database of δ13C values of 16 aroma compounds with respect to their origin (synthetic and natural), and assesses the authenticity of commercially available aroma compounds. Analysis of so-called natural aroma products, revealed δ13C values that were within the expected authentic range although the data did reveal possible falsifications. The sensitivity of the method was evaluated through simple isotope mass balance calculation. Falsification identification is possible for most aromatic substances when the amount of added synthetic compound is in tens of percent.

Activity monitoring is a very important task in lifestyle and health domains where physical activity of a person plays an important role in further reasoning or for providing personalized recommendations. To make such services available to a broader population, one should use devices that most users already have, such as smartphones. Since trends show an increasing popularity of wrist-worn wearables we also consider a sensor-rich wristband as an optional device in this research. We present a real-time activity monitoring algorithm which utilizes data from the smartphone sensors, wristband sensors or their fusion for activity recognition and estimation of energy expenditure of the user. The algorithm detects which devices are present and uses an interval of walking for gravity detection and normalization of the orientation of the devices. The normalized data is afterwards used for the detection of the location of the smartphone on the body, which serves as a context for the selection of location-specific classification model for activity recognition. The recognized activity is finally used for the selection of one or multiple regression models for the estimation of the user’s energy expenditure. To develop the machine-learning models, which can be deployed on the smartphone, we optimized the number and type of extracted features via automatic feature selection. We evaluated each step of the algorithm and each device configuration, and compared the human energy expenditure estimation results against the Bodymedia armband and Microsoft Band 2. We also evaluated the benefit of decision fusion where appropriate. The results show that we achieve a 87% ± 5% average accuracy for activity recognition and that we outperformed both competing devices in the estimation of human energy expenditure by achieving the mean absolute error of 0.6 ± 0.1 MET on average.

Until recently considered as rare, circular RNAs (circRNAs) are emerging as important regulators of gene expression. They are ubiquitously expressed and represent a novel branch of the family of non-coding RNAs. Recent investigations showed that circRNAs are regulated in the cardiovascular system and participate in its physiological and pathological development. In this review article, we will provide an overview of the role of circRNAs in cardiovascular health and disease. After a description of the biogenesis of circRNAs, we will summarize what is known of the expression, regulation and function of circRNAs in the cardiovascular system. We will then address some technical aspects of circRNAs research, discussing how artificial intelligence may aid in circRNAs research. Finally, the potential of circRNAs as biomarkers of cardiovascular disease will be addressed and directions for future research will be proposed. Keywords: Non-coding RNAs, Circular RNAs, Cardiovascular system, Cardiovascular disease, Artificial intelligence, Biomarker

Being able to detect stress as it occurs can greatly contribute to dealing with its negative health and economic consequences. However, detecting stress in real life with an unobtrusive wrist device is a challenging task. The objective of this study is to develop a method for stress detection that can accurately, continuously and unobtrusively monitor psychological stress in real life. First, we explore the problem of stress detection using machine learning and signal processing techniques in laboratory conditions, and then we apply the extracted laboratory knowledge to real-life data. We propose a novel context-based stress-detection method. The method consists of three machine-learning components: a laboratory stress detector that is trained on laboratory data and detects short-term stress every 2 min; an activity recognizer that continuously recognizes the user’s activity and thus provides context information; and a context-based stress detector that uses the outputs of the laboratory stress detector, activity recognizer and other contexts, in order to provide the final decision on 20-min intervals. Experiments on 55 days of real-life data showed that the method detects (recalls) 70% of the stress events with a precision of 95%.

Finding the best classifiers according to different criteria is often performed by a multi-objective machine learning algorithm. This study considers two criteria that are usually treated as the most important when deciding which classifier to apply in practice: comprehensibility and accuracy. A model that offers a broad range of trade-offs between the two criteria is introduced because they conflict; i.e., increasing one decreases the other. The choice of the model is motivated by the fact that domain experts often formalize decisions based on knowledge that can be represented by comprehensible rules and some tacit knowledge. This approach is mimicked by a hybrid tree that consists of comprehensible parts that originate from a regular classification tree and incomprehensible parts that originate from an accurate black-box classifier. An empirical evaluation on 23 UCI datasets shows that the hybrid trees provide trade-offs between the accuracy and comprehensibility that are not possible using traditional machine learning models. A corresponding hybrid-tree comprehensibility metric is also proposed. Furthermore, the paper presents a novel algorithm for learning MAchine LeArning Classifiers with HybrId TrEes (MALACHITE), and it proves that the algorithm finds a complete set of nondominated hybrid trees with regard to their accuracy and comprehensibility. The algorithm is shown to be faster than the well-known multi-objective evolutionary optimization algorithm NSGA-II for trees with moderate size, which is a prerequisite for comprehensibility. On the other hand, the MALACHITE algorithm can generate considerably larger hybrid-trees than a naive exhaustive search algorithm in a reasonable amount of time. In addition, an interactive iterative data mining process based on the algorithm is proposed that enables inspection of the Pareto set of hybrid trees. In each iteration, the domain expert analyzes the current set of nondominated hybrid trees, infers domain relations, and sets the parameters for the next machine learning step accordingly.

In-depth survey for empirical study of the classification-tree comprehensibility.Objective measurements suggest the most influential parameter: the depth of leaves.Number of leaves is a relevant comprehensibility measure only for complex trees.Tree visualization style and layout significantly influence the comprehensibility.Proposed 2 comprehensibility measures considering semantics and structure of the tree. Classification trees are attractive for practical applications because of their comprehensibility. However, the literature on the parameters that influence their comprehensibility and usability is scarce. This paper systematically investigates how tree structure parameters (the number of leaves, branching factor, tree depth) and visualisation properties influence the tree comprehensibility. In addition, we analyse the influence of the question depth (the depth of the deepest leaf that is required when answering a question about a classification tree), which turns out to be the most important parameter, even though it is usually overlooked. The analysis is based on empirical data that is obtained using a carefully designed survey with 98 questions answered by 69 respondents. The paper evaluates several tree-comprehensibility metrics and proposes two new metrics (the weighted sum of the depths of leaves and the weighted sum of the branching factors on the paths from the root to the leaves) that are supported by the survey results. The main advantage of the new comprehensibility metrics is that they consider the semantics of the tree in addition to the tree structure itself.

Multiple Contexts Ensemble (MCE) method to estimate the human energy expenditure (EE).MCE outperforms conventional regression approaches, ensembles and BodyMedia EE device.MCE provides better accuracy than using only the activity of the user as the context.MCE is independent of the machine learning algorithm, thus any algorithm can be used. Monitoring human energy expenditure (EE) is important in many health and sports applications, since the energy expenditure directly reflects the intensity of physical activity. The actual energy expenditure is unpractical to measure; therefore, it is often estimated from the physical activity measured with accelerometers and other sensors. Previous studies have demonstrated that using a person's activity as the context in which the EE is estimated, and using multiple sensors, improves the estimation. In this study, we go a step further by proposing a context-based reasoning method that uses multiple contexts provided by multiple sensors. The proposed Multiple Contexts Ensemble (MCE) approach first extracts multiple features from the sensor data. Each feature is used as a context for which multiple regression models are built using the remaining features as training data: for each value of the context feature, a regression model is trained on a subset of the dataset with that value. When evaluating a data sample, the models corresponding to the context (feature) values in the evaluated sample are assembled into an ensemble of regression models that estimates the EE of the user. Experiments showed that the MCE method outperforms (in terms of lower root means squared error and lower mean absolute error): (i) five single-regression approaches (linear and non-linear); (ii) two ensemble approaches: Bagging and Random subspace; (iii) an approach that uses artificial neural networks trained on accelerometer-data only; and (iv) BodyMedia (a state-of-the-art commercial EE-estimation device).

Deeper searches in game-playing programs relying on the minimax principle generally produce better results. Theoretical analyses, however, suggest that in many cases minimaxing amplifies the noise introduced by the heuristic function used to evaluate the leaves of the game tree, leading to what is known as pathological behavior, where deeper searches produce worse results. In most minimax models analyzed in previous research, positions' true values and sometimes also heuristic values were only losses and wins. In contrast to this, a model is proposed in this paper that uses real numbers for both true and heuristic values. This model did not behave pathologically in the experiments performed. The mechanism that causes deeper searches to produce better evaluations is explained. A comparison with chess is made, indicating that the model realistically reflects position evaluations in chess-playing programs. Conditions under which the pathology might appear in a real-value model are also examined. The essential difference between our real-value model and the common two-value model, which causes the pathology in the two-value model, is identified. Most previous research reports that the pathology tends to disappear when there are dependences between the values of sibling nodes in a game tree. In this paper, another explanation is presented which indicates that in the two-value models the error of the heuristic evaluation was not modeled realistically.